scholarly journals Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

2020 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Reference Frames ◽  
Phenomenal Consciousness ◽  
Predictive Processing ◽  
Active Inference ◽  
Perceptual Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
World Modeling ◽  
Modeling Theory

Integrated World Modeling Theory (IWMT) is a synthetic model that attempts to unify theories of consciousness within the Free Energy Principle and Active Inference framework, with particular emphasis on Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). IWMT further suggests predictive processing in sensory hierarchies may be well-modeled as (folded, sparse, partially disentangled) variational autoencoders, with beliefs discretely-updated via the formation of synchronous complexes—as self-organizing harmonic modes (SOHMs)—potentially entailing maximal a posteriori (MAP) estimation via turbo coding. In this account, alpha-synchronized SOHMs across posterior cortices may constitute the kinds of maximal complexes described by IIT, as well as samples (or MAP estimates) from multimodal shared latent space, organized according to egocentric reference frames, entailing phenomenal consciousness as mid-level perceptual inference. When these posterior SOHMs couple with frontal complexes, this may enable various forms of conscious access as a kind of mental act(ive inference), affording higher order cognition/control, including the kinds of attentional/intentional processing and reportability described by GNWT. Across this autoencoding heterarchy, intermediate-level beliefs may be organized into spatiotemporal trajectories by the entorhinal/hippocampal system, so affording episodic memory, counterfactual imaginings, and planning.

scholarly journals An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation

2020 ◽  
Vol 3 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Subjective Experience ◽  
Reference Frames ◽  
Synthetic Approach ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
World Modeling ◽  
Modeling Theory ◽  
Integrated Information

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement turbo coding via loopy message-passing over predictive (autoencoding) networks, thus generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, thus affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, thus enabling inferential synergy.

scholarly journals An Integrated World Modeling Theory (IWMT) of consciousness: Combining Integrated Information and Global Neuronal Workspace Theories with the Free Energy Principle and Active Inference Framework; towards solving the Hard problem and characterizing agentic causation

2019 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Turbo Codes ◽  
Subjective Experience ◽  
Synthetic Approach ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
World Modeling ◽  
Modeling Theory ◽  
Integrated Information

The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement loopy message passing (cf. turbo-codes) over predictive (autoencoding) networks, so generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, so affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, so enabling inferential synergy.

scholarly journals Integrated World Modeling Theory (IWMT) Expanded: Implications for Theories of Consciousness and Artificial Intelligence

2021 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Graphical Models ◽  
Turbo Codes ◽  
Intelligent Systems ◽  
Causal Structure ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
World Modeling ◽  
Modeling Theory ◽  
Integrated Information

Integrated World Modeling Theory (IWMT) is a synthetic theory of consciousness that uses the Free Energy Principle and Active Inference (FEP-AI) framework to combine insights from Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT’s integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Particular emphasis is placed on the hippocampal/entorhinal system, which may provide a source of high-level reasoning via predictive contrasting and generalized navigation, so affording multiple kinds of conscious access. Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the “Bayesian blur problem”, or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. Finally, I go on to describe parallels between FEP-AI and theories of universal intelligence, including with respect to implications for the future of artificially intelligent systems. Particular emphasis is given to recurrent computation and its relationships with feedforward processing, including potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

scholarly journals Bayesian theories of consciousness: a review in search for a minimal unifying model

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Wiktor Rorot
Keyword(s):  
Free Energy ◽  
Conscious Experience ◽  
Generative Model ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle

Abstract The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience—a Minimal Unifying Model of consciousness.

scholarly journals Predicting green: really radical (plant) predictive processing

2017 ◽  
Vol 14 (131) ◽  
pp. 20170096 ◽  
Author(s):  
Paco Calvo ◽  
Karl Friston
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Local Environment ◽  
Sensory Stimulation ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Adaptive Value ◽  
Plant Behaviour

In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time. We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behaviour takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behaviour. The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly.

scholarly journals The dark room problem in predictive processing and active inference, a legacy of cognitivism?

2019 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Action Learning ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Active Inference ◽  
Energy Principle ◽  
Sensory Stimuli ◽  
Free Energy Principle ◽  
Set Up ◽  
Dark Room

The free energy principle describes cognitive functions such as perception, action, learning and attention in terms of surprisal minimisation. Under simplifying assumptions, agents are depicted as systems minimising a weighted sum of prediction errors encoding the mismatch between incoming sensations and an agent's predictions about such sensations. The ``dark room'' is defined as a state that an agent would occupy should it only look to minimise this sum of prediction errors. This (paradoxical) state emerges as the contrast between the attempts to describe the richness of human and animal behaviour in terms of surprisal minimisation and the trivial solution of a dark room, where the complete lack of sensory stimuli would provide the easiest way to minimise prediction errors, i.e., to be in a perfectly predictable state of darkness with no incoming stimuli. Using a process theory derived from the free energy principle, active inference, we investigate with an agent-based model the meaning of the dark room problem and discuss some of its implications for natural and artificial systems. In this set up, we propose that the presence of this paradox is primarily due to the long-standing belief that agents should encode accurate world models, typical of traditional (computational) theories of cognition.

scholarly journals Modelling ourselves: what the free energy principle reveals about our implicit notions of representation

Synthese ◽  
2021 ◽  
Author(s):  
Matt Sims ◽  
Giovanni Pezzulo
Keyword(s):  
Free Energy ◽  
Philosophy Of Mind ◽  
Predictive Processing ◽  
Energy Principle ◽  
Philosophical Debate ◽  
Free Energy Principle ◽  
Self Organized ◽  
Functional Aspects ◽  
Structural Content ◽  
Unified View

AbstractPredictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to distinguish four main notions of representation, which focus on organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate.

scholarly journals ‘Through others we become ourselves’: The dialectics of predictive coding and active inference

2019 ◽  
Author(s):  
Dimitris Bolis ◽  
Leonhard Schilbach
Keyword(s):  
Free Energy ◽  
Social Interaction ◽  
Predictive Coding ◽  
Real Life ◽  
Other Minds ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Multiscale Dynamics ◽  
Cultural Processes

Thinking Through Other Minds (TTOM) creatively situates the free energy principle within real-life cultural processes, thereby enriching both sociocultural theories and Bayesian accounts of cognition. Here, shifting the attention from thinking to becoming, we suggest complementing such an account by focusing on the empirical, computational and conceptual investigation of the multiscale dynamics of social interaction.

scholarly journals Natural language syntax complies with the free-energy principle

2021 ◽  
Author(s):  
Elliot Murphy ◽  
Emma Holmes ◽  
Karl Friston
Keyword(s):  
Free Energy ◽  
Natural Language ◽  
Explanatory Power ◽  
Speech Segmentation ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Language Functions ◽  
Theoretical Syntax ◽  
Natural Language Syntax

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating elementary syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” and “least effort” criteria from theoretical syntax—adhere to the FEP. This permits a greater degree of explanatory power to the FEP—with respect to higher language functions—and presents linguists with a grounding in first principles of notions pertaining to computability. More generally, we explore the possibility of migrating certain topics in linguistics over to the domain of fields that investigate the FEP, such as complex polysemy. We aim to align concerns of linguists with the normative model for organic self-organisation associated with the FEP, marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

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